Add binary files, app.py and track with Git LFS

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,44 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb.
+
+# %% auto 0
+__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'is_cat', 'classify_image']
+
+# %% ../app.ipynb 6
+from fastai.vision.all import *
+import gradio as gr
+
+def is_cat(x): return x[0].isupper()
+
+
+# %% ../app.ipynb 9
+#EXPORT_PATH = pathlib.Path("model.pkl")
+
+#with set_posix_windows():
+    #learn = load_learner(EXPORT_PATH)
+
+learn = load_learner('model.pkl')
+
+# %% ../app.ipynb 11
+categories = ('Dog', 'Cat')
+def classify_image(img):
+    img = img.resize((192, 192))
+    img = PILImage.create(img)
+
+    if not isinstance(img, Image.Image):
+        return "Error: The input is not a valid image."
+    
+    # Make the prediction using the pre-trained model
+    pred, idx, probs = learn.predict(img)
+    
+    # Return the probabilities as a dictionary of categories and their respective probabilities
+    return dict(zip(categories, map(float, probs)))
+
+
+
+# %% ../app.ipynb 13
+image = gr.Image(type="pil") 
+label = gr.Label()
+examples = ['dog.jpg', 'cat.jpg']
+
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1c09d8f778f89c9eb588eefd9a5ba094eb163cd3ea708a4ea567a83e924f149f
+size 47059947

requirements.txt

@@ -0,0 +1 @@
+fastai